张江江 青年教授
水动力系统调控与河湖复苏
河海大学
张江江
ZHANG Jiangjiang
教育经历
- 2007-2011，浙江大学，环境与资源学院，资源环境科学，学士
- 2011-2017，浙江大学，环境与资源学院，土壤学，博士
工作经历
- 2017-2020，浙江大学，博士后
- 2020- ，河海大学，教师
研究方向
- 水文系统数值建模
- 深度学习
- 数据同化
科研项目
- 地下水污染源与非均质含水层参数联合识别的最优监测网设计与数据同化方法研究
- 长江流域地下水模型构建与应用
- 融合数据和机理的地下水微塑料迁移模拟与数据同化
- 黄淮海地区地下水超采治理与保护关键技术及应用示范
- 非饱和土壤水流和溶质运移的多源多保真度数据同化方法研究
论文信息
- Jiangjiang Zhang, Lingzao Zeng*, Cheng Chen, Dingjiang Chen, and Laosheng Wu (2015), Efficient Bayesian experimental design for contaminant source identification, Water Resources Research, 51(1), 576-598
- Jiangjiang Zhang, Weixuan Li, Lingzao Zeng*, and Laosheng Wu (2016), An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems, Water Resources Research, 52(8), 5971-5984
- Jiangjiang Zhang, Weixuan Li, Guang Lin, Lingzao Zeng*, and Laosheng Wu (2017). Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method. Water Resources Research, 53(3), 1948-1962
- Jiangjiang Zhang, Jun Man, Guang Lin, Laosheng Wu, and Lingzao Zeng* (2018). Inverse modeling of hydrologic systems with adaptive multi-fidelity Markov chain Monte Carlo simulations. Water Resources Research, 54(7), 4867-4886
- Jiangjiang Zhang, Guang Lin*, Weixuan Li, Laosheng Wu, and Lingzao Zeng* (2018). An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions. Water Resources Research, 54(3), 1716-1733
- Jiangjiang Zhang, Qiang Zheng, Dingjiang Chen, Laosheng Wu, and Lingzao Zeng* (2020). Surrogate-Based Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error. Water Resources Research, 56(1), e2019WR025721
- Jiangjiang Zhang, Jasper A. Vrugt*, Xiaoqing Shi, Guang Lin, Laosheng Wu, and Lingzao Zeng* (2020). Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman-Inspired Proposal Distribution. Water Resources Research, 56(3), e2019WR025474
- Jiangjiang Zhang, Qiang Zheng, Laosheng Wu, and Lingzao Zeng* (2020). Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization. Water Resources Research, 56(12), e2020WR027399
- Jiangjiang Zhang*, Chenglong Cao, Tongchao Nan, Lei Ju, Hongxiang Zhou, and Lingzao Zeng (2024), A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems. Water Resources Research, 60(2), e2023WR035389 (WRR亮点文章)
- Chenglong Cao, Jiangjiang Zhang*, Wei Gan, Tongchao Nan, and Chunhui Lu (2024). A Deep Learning-Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non-Linearity and Non-Gaussianity Challenges. Water Resources Research, 60(7), e2023WR036899（通讯作者）
- Tongchao Nan, Jiangjiang Zhang*, Yifan Xie, Chenglong Cao, Jichun Wu, Chunhui Lu (2024). Effective Characterization of Fractured Media with PEDL: A Deep Learning-Based Data Assimilation Approach. Water Resources Research, 60(7), e2023WR036673（通讯作者）
- Jinhua Wen, Jiangjiang Zhang*, Shuiping Yao, and Feifei Zheng (2024), Trends and Drivers of Water Use Change in Economic Activities of Zhejiang Province, China, Before and During the COVID-19 Pandemic. Journal of Hydrology, 631, 130830（通讯作者）
- Feifei Zheng, Hang Yin, Jiangjiang Zhang*, Huan-Feng Duan, and Hoshin V Gupta (2024). A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity. Journal of Hydrology, 640, 131706（通讯作者）
- Lei He, Huan Cheng, Zhengnian Nan, Yiqing Gong, Huifang Guo, Jingqiao Mao, and Jiangjiang Zhang* (2025). Improving joint identification of groundwater contaminant source and non-Gaussian distributed conductivity field using a deep learning-based ensemble smoother. Journal of Hydrology, 658, 133202（通讯作者）
- Zijie Tang, Jianyun Zhang, Mengliu Hu, Zhongrui Ning, Jiayong Shi, Ran Zhai, Cuishan Liu, Jiangjiang Zhang*, and Guoqing Wang* (2024). Improving streamflow forecasting in semi-arid basins by combining data segmentation and attention-based deep learning. Journal of Hydrology, 643, 131923（共同通讯）
- Hongzhe Pan, Yiping Li*, Jiangjiang Zhang*, Chenglong Cao, Yu Cheng, Yuxuan Zhou, Yaning Wang, Song Bai, Jun Liu, Qiaoyi Jin, Xiuquan Zhu (2025). Identifying urban river pollution sources from wet-weather discharges using an integrated deep learning and data assimilation approach, Journal of Hydrology, 661, 133797（共同通讯）
- Shuyou Zhang#, Jiangjiang Zhang#, Lili Niu, Qiang Chen, Qing Zhou, Nan Xiao, Jun Man, Jianqing Ma, Changlong Wei, Songhe Zhang, Yongming Luo, and Yijun Yao* (2024). Escalating Arsenic Contamination Throughout Chinese Soils. Nature Sustainability, 7, 766-775（共同一作）
- Qing Zhou#, Jiangjiang Zhang#, Shuyou Zhang#, Qiang Chen, Huifeng Fan, Chenglong Cao, Yanni Zhang, Yadi Yang, Jian Luo*, and Yijun Yao* (2025). Groundwater Quality Evolution Across China. Nature Communications, 16, 2522（共同一作）
- Lei Yao, Jiangjiang Zhang*, Chenglong Cao, and Feifei Zheng (2025). Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates. Environmental Modelling & Software, 185, 106332（通讯作者）
- Weiya Xu*, Changhao Lyu, Jiangjiang Zhang*, Huanling Wang, Rubin Wang, Long Yan, and Wei-Chau Xie (2024). Calibrating high-dimensional rock creep constitutive models for geological disaster prevention: An application of data assimilation methods. International Journal of Rock Mechanics and Mining Sciences, 183, 105911（共同通讯）
- Man Jun, Guo Yuanming, Jin Junliang, Zhang Jianyun, Yao Yijun*, and Zhang Jiangjiang* (2021). Characterization of vapor intrusion sites with a deep learning-based data assimilation method. Journal of Hazardous Materials, 431, 128600（共同通讯）
- Qing Zhou#, Jiangjiang Zhang#, Ke Xing, Jing Wei, and Yijun Yao (2024). Groundwater Nitrate Contamination in China: Spatial Distribution, Temporal Trend, and Driver Analysis. Environmental Research, 262, 119932（共同一作）
社会兼职
- Journal of Hydrology
- Scientific Reports
- Earth Critical Zone
- Frontiers in Water
荣誉信息
- 江苏省“双创博士”